Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for classifying a venue by analyzing venue data from a venue website, comprising: receiving preliminary venue-related data including a venue URL; scanning the venue website to retrieve venue data; retrieving verifiable venue data from the venue data, the verifiable venue data being a subset of the venue data; analyzing, using a computer, the verifiable venue data by comparing the verifiable venue data to the preliminary venue-related data; determining a probability level for the venue URL from the comparison; if the probability level for the venue URL is equal or greater than a first probability level, determining the number of selected attributes in the venue data; determining the percentage of the attribute representation from the total number of preselected attributes in the venue data; and classifying the venue based on the percentage of the attribute representation.
A method for classifying a venue from its website data involves first receiving preliminary data about the venue, including its URL. The venue's website is then scanned to retrieve data from HTML, text, PDF documents, and images. Verifiable data, a subset of the total data, is extracted. This verifiable data is analyzed by comparing it to the preliminary data to determine a probability level for the URL's validity. If this probability exceeds a threshold, the system counts selected attributes found on the website. Finally, the venue is classified based on the percentage of these found attributes relative to a set of preselected, expected attributes.
2. The method of claim 1 , further comprising: determining attribute distance association by identifying correlation of attributes; quantifying attribute similarities from the attribute distance association; comparing the classified venue to other venues based on quantified attribute similarities.
The venue classification method, as described in Claim 1, further includes determining attribute distance association by identifying correlations between different attributes found on venue websites. Attribute similarities are then quantified based on these distance associations. The classified venue is then compared to other venues based on these quantified attribute similarities, allowing for a determination of how similar or different venues are based on their attribute profiles.
3. The method of claim 1 , further comprising: classifying the atmosphere of the venue based on the attributes.
The venue classification method, as described in Claim 1, also classifies the atmosphere of the venue based on the attributes extracted from the website. For example, attributes like "live music," "dim lighting," and "craft cocktails" might suggest a "romantic" or "lively" atmosphere, while attributes like "arcade games" and "pizza" might suggest a "family-friendly" atmosphere.
4. The method of claim 1 , wherein the classification of the venue further comprises: assigning a classifier factor for each selected attribute in the venue data; classifying the venue from the assigned classifier factors, wherein the classifier factor is an activity factor.
The venue classification method, as described in Claim 1, involves assigning a classifier factor, specifically an activity factor, to each selected attribute found in the venue data. The final venue classification is then determined from these assigned classifier factors. This activity factor could represent the type of activity the venue is associated with, such as "dining," "entertainment," or "nightlife," and the venue is classified accordingly.
5. A computer-implemented method for determining attributes of a venue, the method comprising steps to: analyze first data associated with a first venue to identify a first set of venue attributes associated with the first venue; analyze second data associated with a second venue to identify a second set of venue attributes associated with the second venue; compare, using a computing device, the first set of venue attributes with the second set of venue attributes; and determine, based on comparing the first set and the second set, a level of similarity between the first venue and the second venue.
A computer-implemented method determines the similarity between venues by first analyzing data from a first venue to identify its attributes. The method then analyzes data from a second venue to identify its attributes. A computing device then compares the sets of attributes from the two venues. Based on this comparison, the method determines a level of similarity between the first venue and the second venue.
6. The method of claim 5 , wherein one or more venue attributes of the first set are identified based on an existence of synonymous words in the first data.
In the venue attribute determination method described in Claim 5, the identification of venue attributes for the first venue includes identifying attributes based on the existence of synonymous words within the venue's data. For example, the presence of either "couch" or "sofa" may indicate the venue has "comfortable seating."
7. The method of claim 5 , the method further comprising steps to: determine a first number of occurrences that a first venue attribute of the first set appears in the data.
In the venue attribute determination method described in Claim 5, the method further involves determining the number of times each venue attribute appears in the venue's data. Specifically, the method counts the number of occurrences of the first venue attribute of the first venue's data.
8. The method of claim 7 , wherein the first number of occurrences includes occurrences of a first word and a second word in the data, wherein the first word and the second word have synonymous meanings.
In the venue attribute determination method described in Claim 7, the count of occurrences for a particular attribute includes counting both a primary word for that attribute and any of its synonyms. For example, if searching for the attribute "drinks," the method counts both occurrences of the word "drinks" and occurrences of the word "beverages."
9. The method of claim 7 , the method further comprising steps to: determine, for each remaining venue attribute of the first set, a respective number of occurrences that the respective venue attribute appears in the data.
In the venue attribute determination method described in Claim 7, after determining the number of occurrences for the first attribute, the method determines the number of occurrences for each of the remaining attributes of the first venue.
10. The method of claim 9 , the method further comprising steps to: sum the first number of occurrences with each of the respective number of occurrences to achieve a total number of occurrences; determine a first ratio between the first number of occurrences and the total number of occurrences; and determine a relative attribute prominence for the first venue attribute based on the first ratio and other data associated with other venues.
In the venue attribute determination method described in Claim 9, the method calculates a total count of attribute occurrences by summing the number of occurrences of the first attribute with the number of occurrences of each of the other attributes. The method then calculates a ratio between the number of occurrences of the first attribute and the total number of attribute occurrences. This ratio, along with data from other venues, is used to determine the attribute's prominence relative to other venues.
11. The method of claim 5 , the method further comprising steps to: determine a first inferred attribute of the venue based on the data, wherein the venue attributes include the first inferred attribute.
In the venue attribute determination method described in Claim 5, the method also determines inferred attributes of the venue based on the available data. The complete set of venue attributes then includes these inferred attributes in addition to the directly observed ones.
12. The method of claim 11 , the method further comprising steps to: determine the first inferred attribute based on an existence of a first venue attribute and a second venue attribute in the first set of venue attributes.
In the venue attribute determination method described in Claim 11, the determination of inferred attributes is based on the presence of other attributes within the same venue. If venue data includes attributes A and B, an inferred attribute C is determined based on the correlation between A and B.
13. The method of claim 12 , the method further comprising steps to: determine the first inferred attribute based on a comparison between a first number of occurrences of the first venue attribute in the data and a second number of occurrences of the second venue attribute in the data.
In the venue attribute determination method described in Claim 12, the method determines the inferred attribute by comparing the number of times attribute A appears in the data with the number of times attribute B appears in the data. If attribute A appears significantly more often than attribute B, a different inferred attribute might be deduced than if the reverse were true.
14. The method of claim 11 , the method further comprising steps to: determine the first inferred attribute based on there not being a particular venue attribute among the first set of venue attributes.
In the venue attribute determination method described in Claim 11, an inferred attribute can be determined based on the *absence* of a specific attribute. For example, if a restaurant's website does *not* mention "children's menu," it might be inferred that the venue is not particularly family-friendly.
15. The method of claim 11 , the method further comprising steps to: determine the first inferred attribute based on an existence, in the data, of a first word from among a group of synonymous words.
In the venue attribute determination method described in Claim 11, the determination of inferred attributes can be based on the specific choice of words used to describe an attribute, particularly if those words are synonyms.
16. The method of claim 15 , wherein the first word is a fancy or elegant word compared to other synonymous words in the group, and wherein the first inferred attribute indicates that the venue is high-end or upscale compared to other venues.
In the venue attribute determination method described in Claim 15, if the description of a venue uses a "fancy" or "elegant" word from a group of synonyms, the method infers that the venue is high-end or upscale compared to other venues. For example, using the word "lavatory" instead of "toilet".
17. The method of claim 15 , the method further comprising steps to: determine a different inferred attribute for a different venue based on an existence, in different data associated with the different venue, of a second word from among the group of synonymous words.
In the venue attribute determination method described in Claim 15, a *different* inferred attribute can be determined for a different venue based on the usage of a *different* word from the same group of synonyms. This allows for nuance in determining the atmosphere of different venues.
18. The method of claim 5 , the method further comprising steps to: identify a first venue attribute from the first set of venue attributes; determine an attribute distance association related to a co-occurrence of the first venue attribute and a different venue attribute in other data associated with other venues.
In the venue attribute determination method described in Claim 5, the method identifies an attribute from the first set of attributes and then determines an attribute distance association related to how often that attribute co-occurs with *another* attribute in the data of *other* venues.
19. The method of claim 18 , the method further comprising steps to: identify one or more venues from the other venues that are associated with the different venue attribute; and determine levels of similarity between the first venue and the one or more venues based on the attribute distance association relating to the first venue attribute and the different venue attribute.
In the venue attribute determination method described in Claim 18, the method identifies other venues associated with the "different" attribute and then determines the levels of similarity between the first venue and these other venues. This similarity determination is based on the attribute distance association, specifically the co-occurrence of the initial attribute and the "different" attribute.
20. The method of claim 5 , the method further comprising steps to: analyze other data associated with other venues to determine possible attributes from which the venue attributes are identified.
In the venue attribute determination method described in Claim 5, before identifying attributes for the first venue, the method analyzes data from *other* venues to determine a pool of *possible* attributes from which the venue attributes will be identified. This creates a standardized set of attributes for comparison.
21. A system for determining attributes of a venue, the system comprising one or more processors that are operable to: analyze first data associated with a first venue to identify a first set of venue attributes associated with the first venue; analyze second data associated with a second venue to identify a second set of venue attributes associated with the second venue; compare, using a computing device, the first set of venue attributes with the second set of venue attributes; and determine, based on comparing the first set and the second set, a level of similarity between the first venue and the second venue.
A system for determining the similarity of venues includes one or more processors that: analyze data associated with a first venue to identify its attributes; analyze data associated with a second venue to identify its attributes; compare the attribute sets of the two venues; and determine a level of similarity between the first and second venue based on that comparison.
22. The system of claim 21 , wherein the one or more processors are further operable to: determine a first number of occurrences that a first venue attribute of the first set appears in the data, wherein the first number of occurrences includes occurrences of a first word and a second word in the data, wherein the first word and the second word have synonymous meanings; determine, for each remaining venue attribute of the first set, a respective number of occurrences that the respective venue attribute appears in the data; sum the first number of occurrences with each of the respective number of occurrences to achieve a total number of occurrences; determine a first ratio between the first number of occurrences and the total number of occurrences; determine a relative attribute prominence for the first venue attribute based on the first ratio and other data associated with other venues; determine a first inferred attribute of the venue based on the data, wherein the venue attributes include the first inferred attribute; determine the first inferred attribute based on an existence of a first venue attribute and a second venue attribute in the first set of venue attributes; determine the first inferred attribute based on a comparison between a first number of occurrences of the first venue attribute in the data and a second number of occurrences of the second venue attribute in the data; identify a first venue attribute from the first set of venue attributes; determine an attribute distance association related to a co-occurrence of the first venue attribute and a different venue attribute in other data associated with other venues; identify one or more venues from the other venues that are associated with the different venue attribute; and determine levels of similarity between the first venue and the one or more venues based on the attribute distance association relating to the first venue attribute and the different venue attribute.
The venue similarity determination system described in Claim 21 is further configured to: count the occurrences of attributes, including synonyms; calculate the ratio of each attribute's occurrences to the total number of attribute occurrences; determine the relative prominence of an attribute compared to other venues; determine inferred attributes based on the presence or absence of other attributes, and/or the specific language used; determine attribute distance association based on attribute co-occurrence in other venues; identify venues associated with co-occurring attributes; and determine similarity levels based on the attribute distance association.
23. A computer program product comprising a non-transitory computer usable medium having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method for determining attributes of a venue, the method comprising steps to: analyze first data associated with a first venue to identify a first set of venue attributes associated with the first venue; analyze second data associated with a second venue to identify a second set of venue attributes associated with the second venue; compare, using a computing device, the first set of venue attributes with the second set of venue attributes; and determine, based on comparing the first set and the second set, a level of similarity between the first venue and the second venue.
A computer program product residing on a non-transitory computer-readable medium is configured to determine the similarity between venues. The program analyzes data from a first venue to identify its attributes, analyzes data from a second venue to identify its attributes, compares the attributes, and determines a level of similarity based on that comparison.
24. The computer program product of claim 23 , the method further comprising steps to: determine a first number of occurrences that a first venue attribute of the first set appears in the data, wherein the first number of occurrences includes occurrences of a first word and a second word in the data, wherein the first word and the second word have synonymous meanings; determine, for each remaining venue attribute of the first set, a respective number of occurrences that the respective venue attribute appears in the data; sum the first number of occurrences with each of the respective number of occurrences to achieve a total number of occurrences; determine a first ratio between the first number of occurrences and the total number of occurrences; determine a relative attribute prominence for the first venue attribute based on the first ratio and other data associated with other venues; determine a first inferred attribute of the venue based on the data, wherein the venue attributes include the first inferred attribute; determine the first inferred attribute based on an existence of a first venue attribute and a second venue attribute in the first set of venue attributes; determine the first inferred attribute based on a comparison between a first number of occurrences of the first venue attribute in the data and a second number of occurrences of the second venue attribute in the data; identify a first venue attribute from the first set of venue attributes; determine an attribute distance association related to a co-occurrence of the first venue attribute and a different venue attribute in other data associated with other venues; identify one or more venues from the other venues that are associated with the different venue attribute; and determine levels of similarity between the first venue and the one or more venues based on the attribute distance association relating to the first venue attribute and the different venue attribute.
The venue similarity determination computer program described in Claim 23 is further configured to: count the occurrences of attributes, including synonyms; calculate the ratio of each attribute's occurrences to the total number of attribute occurrences; determine the relative prominence of an attribute compared to other venues; determine inferred attributes based on the presence or absence of other attributes, and/or the specific language used; determine attribute distance association based on attribute co-occurrence in other venues; identify venues associated with co-occurring attributes; and determine similarity levels based on the attribute distance association.
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December 23, 2014
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